Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Designing Committees for Mitigating Biases
Authors: Michal Feldman, Yishay Mansour, Noam Nisan, Sigal Oren, Moshe Tennenholtz1942-1949
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study a novel model of voting in which a committee of experts is constructed to reduce the biases of its members. We ο¬rst present voting rules that optimally reduce the biases of a given committee. Our main results include the design of committees, for several settings, that are able to reach a nearly optimal (unbiased) choice. We also provide a thorough analysis of the trade-offs between the committee size and the obtained error. |
| Researcher Affiliation | Collaboration | 1Tel-Aviv University, Israel, 2Microsoft Research, Israel, 3Google Research, Israel, 4Hebrew University, Israel 5Ben-Gurion University of the Negev, Israel, 6Technion Israel Institute of Technology, Israel |
| Pseudocode | No | The paper describes computational procedures and reductions (e.g., building a directed graph and solving shortest paths in Theorem 3.2) in paragraph form, but it does not present them as structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the release of source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not mention the use of any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe data partitioning into training, validation, or test sets. |
| Hardware Specification | No | The paper is theoretical and does not describe the hardware used for any experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setup details such as hyperparameters or training configurations. |